Transcript Proposition Bank: a resource of predicate

Computational Lexical Semantics
Martha Palmer
Vilem Mathesius Lecture Series 21
Charles University, Prague
December, 2006
Prague, Dec, 2006
Meaning?

Complete representation of real world knowledge Natural Language Understanding?
NLU


Only build useful representations for small vocabularies
Major impediment to accurate Machine Translation,
Information Retrieval and Question Answering
Ask Jeeves – A Q/A, IR ex.
What do you call a successful movie? Blockbuster

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Tips on Being a Successful Movie Vampire ... I shall call
the police.
Successful Casting Call & Shoot for ``Clash of Empires'' ...
thank everyone for their participation in the making of
yesterday's movie.
Demme's casting is also highly entertaining, although I
wouldn't go so far as to call it successful. This movie's
resemblance to its predecessor is pretty vague...
VHS Movies: Successful Cold Call Selling: Over 100 New
Ideas, Scripts, and Examples from the Nation's Foremost
Sales Trainer.
Ask Jeeves – filtering w/ POS tag
What do you call a successful movie?


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
Tips on Being a Successful Movie Vampire ... I shall call
the police.
Successful Casting Call & Shoot for ``Clash of Empires'' ...
thank everyone for their participation in the making of
yesterday's movie.
Demme's casting is also highly entertaining, although I
wouldn't go so far as to call it successful. This movie's
resemblance to its predecessor is pretty vague...
VHS Movies: Successful Cold Call Selling: Over 100 New
Ideas, Scripts, and Examples from the Nation's Foremost
Sales Trainer.
Filtering out “call the police”
Different senses,
- different syntax,
- different kinds of participants,
- different types of propositions.
call(you,movie,what) ≠ call(you,police)
you movie what
you
police
Outline
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Linguistic Theories of semantic representation
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Case Frames – Fillmore – FrameNet
Lexical Conceptual Structure – Jackendoff – LCS
Proto-Roles – Dowty – PropBank
English verb classes (diathesis alternations) Levin - VerbNet
Talmy, Levin and Rappaport
Manual Semantic Annotation
Automatic Semantic annotation
The Case for Case
Charles J. Fillmore
in E. Bach and R.T. Harms, eds. Universals in Linguistic
Theory, 1-88. New York: Holt, Rinehart and Winston.
Thanks to Steven Bethard
Prague, Dec, 2006
Case Theory

Case relations occur in deep-structure
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Surface-structure cases are derived
A sentence is a verb + one or more NPs

Each NP has a deep-structure case
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A(gentive)
I(nstrumental)
D(ative)
F(actitive)
L(ocative)
O(bjective)
Subject is no more important than Object

Subject/Object are surface structure
Case Selection

Noun types
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
Different cases require different nouns
E.g.
N  [+animate]/A,D[X__Y]
Verb frames
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Verbs require arguments of particular cases
E.g.

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
sad [ __D]
give [ __O+D+A]
open [ __O(I)(A)]
Case Theory Benefits
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Fewer tokens
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Fewer verb senses
E.g. cook [ __O(A)] covers
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Fewer types
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Mother is cooking the potatoes
The potatoes are cooking
Mother is cooking
“Different” verbs may be the same semantically, but with different
subject selection preferences
E.g. like and please are both [ __O+D]
Only noun phrases of the same case may be conjoined
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*John and a hammer broke the window
*The car broke the window with a fender
Case Theory Drawbacks
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How can a handful of cases cover every
possible type of verb argument?
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Is an agent always animate? Always volitional?
Is an instrument always an artifact?
What are the mapping rules from syntax to
semantics?
FrameNet
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Baker, Collin F., Charles J. Fillmore, and John
B. Lowe. (1998) The Berkeley FrameNet
project. In Proceedings of COLING/ACL-98 ,
pages 86--90, Montreal.
Fillmore, Charles J. and Collin F. Baker.
(2001). Frame semantics for text
understanding. In the Proceedings of NAACL
WordNet and Other Lexical Resources
Workshop Pittsburgh, June.
Introducing FrameNet
Thanks to Chuck Fillmore and Collin Baker
In one of its senses, the verb observe evokes a frame
called Compliance: this frame concerns people’s responses
to norms, rules or practices.
The following sentences illustrate the use of the verb in the
intended sense:
 Our family observes the Jewish dietary laws.
 You have to observe the rules or you’ll be penalized.
 How do you observe Easter?
 Please observe the illuminated signs.
FrameNet
FrameNet records information about English
words in the general vocabulary in terms of
1.
2.
3.
theta
the frames (e.g. Compliance) that they evoke,
the frame elements (semantic roles) that make up the
components of the frames (in Compliance, Norm is
one such frame element), and
each word’s valence possibilities, the ways in which
information about the frames is provided in the linguistic
structures connected to them (with observe, Norm is
typically the direct object).
The FrameNet Product
The FrameNet database constitutes

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a set of frame descriptions
a set of corpus examples annotated with respect to
the frame elements of the frame evoked by each
lexical unit
lexical entries, including definitions and displays of
the combinatory possibilities of each lexical unit, as
automatically derived from the annotations
a display of frame-to-frame relations, showing how
some frames are elaborations of others, or are
components of other frames.
Frame Elements for Compliance
The frame elements that figure in the
Compliance frame are called
Norm (the rule, practice or convention)
 Protagonist (the person[s] reacting to the
Norm)
 Act (something done by the Protagonist that is
evaluated in terms of the Norm)
 State_of_affairs (a situation evaluated in
terms of the Norm)

- You do a whole frame for just observe?
- No. There are other Compliance words too.
V - adhere, comply, conform, follow, heed, obey, submit, ...;
AND NOT ONLY VERBS
N - adherence, compliance, conformity, obedience,
observance, ...;
A - compliant, obedient, ...;
PP - in compliance with, in conformity to, ...;
AND NOT ONLY WORDS FOR POSITIVE RESPONSES TO NORMS
V - break, disobey, flout, transgress, violate ,...;
N - breach, disobedience, transgression, violation,...;
PP - in violation of, in breach of, ...
Tagging Compliance sentences
Protagonist
State_of_affairs
Our family
The light switches in
this room
observes
are in full conformity
the dietary laws
with the building code
Norm
Norm
- Are we finished with the verb observe?
- No. This verb has several other meanings too.
 In the Perception_active frame we get the
uses seen in observing children at play,
observing an ant colony, sharing frame
membership with watch, attend, listen to,
view & pay attention.
 In a Commenting frame, observe and
observation share frame membership with
remark & comment.
Lexical Unit
Our unit of description is not the word (or
“lemma”) but the lexical unit (Cruse 1986), – a
pairing of a word with a sense. In our terms this is
the pairing of a word with a single frame.
The lexical unit - roughly equivalent to a word in
a synset - is the unit in terms of which important
generalizations about lexical relations, meanings
and syntactic behavior can best be formulated.
LUs and V-N relationships
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Note that the nouns based on observe are
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Similarly, the nouns based on adhere are
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observance in the Compliance frame,
observation in the Perception_active frame
adherence in the Compliance frame,
adhesion in the Attachment frame.
When we need to be precise we show the framespecific sense of a lemma (the full name of an
LU) with a dotted expression:

Compliance.observe, Attachment.adhere, etc.
words, frames, lexical units
Compliance
observance
Perception
observe
observation
2 lexical units sharing same form:
Compliance.observe,
Perception.observe
words, frames, lexical units
Compliance
adherence
Attachment
adhere
adhesion
2 lexical units sharing the same form:
Compliance.adhere,
Attachment.adhere
The study of polysemy concerns
membership in different frames
Compliance
Perception
observe
Commenting
Different LU, Different Valence
Compliance.observe generally has an NP as its
direct object.
Perception.observe has these patterns:
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NP: Observe the clouds overhead.
NP+Ving: I observed the children playing.
wh-clause: Observe what I’m doing.
that-clause: We observed that the process terminated
after an hour.
Comment.observe occurs frequently with a quoted
comment:

“That was brilliant,” he observed snidely.
Lexical-units: Wrap-up
Lexical units are the entities with respect to which we define
 meanings
 grammatical behavior
 semantic relations with other entities
 morphological relations with other entities
In short, there aren’t interesting things to say about the verb
observe in general, but only about the individual lexical units
that happen to have the form observe.
Jackendoff: Lexical Conceptual Structures
from Jackendoff, R.S., Towards an
Explanatory Semantic Representation,
Linguistic Inquiry, 7:1, pp. 89-150, 1976.
Prague, Dec, 2006
Semantic Decomposition
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Markers
HORSE
RED

the red horse
Functions
SEE(x,y)
the man saw the (red) horse
SEE(x,HORSE)
SEE(THE MAN,THE HORSE)
SEE(X1, Y1)
(What is the value? predicates? )
Five Semantic Functions
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GO
BE
STAY
LET
CAUSE
GO – Change of location
The train traveled from Detroit to Cincinatti.
The hawk flew from its next to the ground.
An apple fell from the tree to the ground.
The coffee filtered from the funnel into the cup.
GO (x,y,z)
THROUGH THE AIR/DOWNWARD
THEME GOES FROM SOURCE, TO GOAL
Mapping from Syntax to Semantics
/fli/
+V
+ [NP1____ (from NP2) (to NP3)]
GO (NP1,NP2,NP3)
THROUGH THE AIR
BE – Stationary location
Max is in Africa.
The vine clung to the wall.
The dog is on the left of the cat.
The circle contains/surrounds the dot?
BE(x,y)
THEME IS AT LOCATION
BE (THE DOG, LEFT OF (THE CAT))
STAY – Durational stationary location
The bacteria stayed in his body.
Stanley remained in Africa.
Bill kept the book on the shelf.
STAY(x,y)
THEME IS AT LOCATION for a duration
STAY (STANLEY, AFRICA) (for two years)
Locational modes: POSIT, POSS, ID
The train traveled from Detroit to Cincinatti.
GO (x,y,z)
POSIT
Harry gave the book to the library.
GO (x,y,z)
POSS
The book belonged to the library..
BE (x,z)
POSS
Locational modes: POSIT, POSS, ID
The bacteria stayed in his body.
STAY (x,z)
POSIT
The library kept the book.
STAY (x,z)
POSS
Locational modes: POSIT, POSS, ID
*The coach changed from a handsome young man
to a pumpkin.
[GOIDENT (x,y,z)]
Princess Mia changed from an ugly duckling into a
swan.
[GOIDENT (x,y,z)]
Universal grammar?
Causation and Permission: CAUSE and LET
The rock fell from the roof to the ground.
[GOPOSIT (x,y,z)]
Linda lowered the rock from the roof to the ground.
[CAUSE (a, GOPOSIT (x,y,z))]
Linda dropped the rock from the roof to the ground.
[LET (a, GOPOSIT (x,y,z))]
INSTRUMENTS
Linda lowered the rock from the roof to the ground
with a cable.
CAUSE (a, GOPOSIT (x,y,z))
Inst: i
Instruments only occur with causation.
CAUSE always has an event second
argument.
Lexical Conceptual Structure
concept
GO
motional
BE
punctual
STAY
durational
POSIT
go
fall
be
contain
stay
remain
CAUSE(a,GO)
bring, take
CAUSE(a,STAY) keep, hold
LET(a,GO)
LET(a,BE)
POSS
receive
inherit
have
own
keep
IDENT
become
change
be
seem
stay
remain
obtain, give make,elect
keep, retain keep
drop,release accept,
leave, allow fritter, permit leave
Rules of inference
CAUSE(a, event) -> event.
Machine Translation:
Interlingual Methods
Bonnie J. Dorr, Eduard H.
Hovy, Lori S. Levin
Thanks to Les Sikos
Prague, Dec, 2006
Overview
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What is Machine Translation (MT)?
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Automated system
Analyzes text from Source Language (SL)
Produces “equivalent” text in Target Language
(TL)
Ideally without human intervention
Source
Language
Target
Language
Overview
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Three main methodologies for Machine
Translation
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Direct
Transfer
Interlingual
Overview

Three main methodologies for Machine
Translation
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Direct
Transfer
Interlingual
Overview

Three main methodologies for Machine
Translation
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
Direct
Transfer
Interlingual
Overview

Three main methodologies for Machine
Translation
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Direct
Transfer
Interlingual
Overview
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Interlingua
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Single underlying representation for both SL and
TL
which ideally
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
Abstracts away from language-specific characteristics
Creates a “language-neutral” representation
Can be used as a “pivot” representation in the translation
Overview

Cost/Benefit analysis of moving up the
triangle
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Benefit


Reduces the amount of work required to traverse
the gap between languages
Cost

Increases amount of analysis


Convert the source input into a suitable
pre-transfer representation
Increases amount of synthesis

Convert the post-transfer representation
into the final target surface form
Overview

Two major advantages of Interlingua method
1.
The more target languages there are, the more
valuable
TL1
an Interlingua becomes
Source
Language
InterLingua
TL2
TL3
TL4
TL5
TL6
Overview
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Two major advantages of Interlingua method
2.
Interlingual representations can also be used by
NLP systems for other multilingual applications
Overview

Sounds great, but…due to many complexities

Only one interlingual MT system has ever been
made operational in a commercial setting
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KANT (Nyberg and Mitamura, 1992, 2000;
Lonsdale et al., 1995)

Only a few have been taken beyond research
prototype
Issues

Loss of Stylistic Elements

Because representation is independent of syntax
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Generated target text reads more like a paraphrase
Style and emphasis of the original text are lost
Not so much a failure of Interlingua as
incompleteness
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
Caused by a lack of understanding of discourse and
pragmatic elements required to recognize and
appropriately reproduce style and emphasis
In some cases it may be an advantage to ignore the
author’s style

Outside the field of artistic texts (poetry and fiction)
syntactic form of source text is superfluous
Issues

Loss of Stylistic Elements

Current state of the art

It is only possible to produce reliable interlinguas
between language groups (e.g., Japanese –
Western European) within specialized domains
Issues

Linguistic Divergences

Structural differences between languages

Categorical Divergence

Translation of words in one language into words that have
different parts of speech in another language
 To be jealous
 Tener celos (To have jealousy)
Issues

Linguistic Divergences

Conflational Divergence

Translation of two or more words in one language into
one word in another language
 To kick
 Dar una patada (Give a kick)
Issues

Linguistic Divergences

Structural Divergence

Realization of verb arguments in different
syntactic configurations in different languages
 To enter the house
 Entrar en la casa (Enter in the house)
Issues

Linguistic Divergences

Head-Swapping Divergence

Inversion of a structural-dominance relation between two
semantically equivalent words
 To run in
 Entrar corriendo (Enter running)
Issues

Linguistic Divergences

Thematic Divergence

Realization of verb arguments that reflect different
thematic to syntactic mapping orders
 I like grapes
 Me gustan uvas (To-me please grapes)
Issues

Linguistic Divergences may be the norm
rather than
the exception

Differences in MT architecture (direct, transfer,
interlingual) are crucial for resolution of
cross-language divergences

Interlingua approach takes advantage of the
compositionality of basic units of meaning
to resolve divergences
Issues

For example:
To kick – Dar una patada (Give a kick)

Conflational divergence can be resolved by
mapping English kick into two components before
translating
into in Spanish


Motional component (movement of the leg)
Manner component (a kicking motion)
Current Efforts

KANT system (Nyberg and Mitamura, 1992)

Only interlingual MT system that has ever been
made operational in a commercial setting




Caterpillar document workflow (mid-90s)
Knowledge-based system
Designed for translation of technical documents
written in Caterpillar Technical English (CTE) to
French, Spanish, and German
Controlled English – no pronouns, conjunctions,...
Current Efforts

Pangloss project (Frederking et al., 1994)
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Ambitious attempt to build rich interlingual
expressions
Uses humans to augment system analysis
Representation includes a set of frames for
representing semantic components, each of
which
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
Are headed by a unique identifier
And have a separate frame with aspectual information
(duration, telicity, etc.)
Some modifiers are treated as scalars and
represented by numerical values
Current Efforts

Mikrokosmos (Mahesh and Nirenburg, 1995) /
OntoSem (Nirenburg and Raskin, 2004)



Focus is to produce semantically rich Text-Meaning
Representations (TMRs) of text
TMRs use a language-independent metalanguage
also used for static knowledge resources
TMRs aimed at the most difficult problems of NLP


Disambiguation, reference resolution
Goal is to populate a fact repository with TMRs as a
language-independent search space for questionanswering and knowledge-extraction applications
Current Efforts

PRINCITRAN (Dorr & Voss, 1996)

Approach assumes an interlingua derived from
lexical semantics and predicate decomposition


Jackendoff 1983, 1990; Levin & Rappaport-Hovav 1995a, 1995b
Has not complicated, but rather facilitated, the
identification and construction of systematic
relations at the interface between each level
Current Efforts

Motivation for Non-Uniform Approach
German: Der Berg liegt im Suden der Stadt

Ambiguous in English:
The mountain lies in the south of the city
 The mountain lies to the south of the city


In other words, the German phrase maps to two
distinct representations
Current Efforts

Using Default knowledge in the KR
Mountains are physical entities, typically distinct
and external to cities
 System chooses second translation



The mountain lies to the south of the city
Using specific facts in the KR
A particular mountain is in the city
 System overrides default knowledge and chooses
first translation


The mountain lies in the south of the city
Current Efforts

The need to translate such sentences
accurately is a clear case of where general as
well as specific real-world knowledge should
assist in eliminating inappropriate translations

Knowledge Representational level, not the
Interlingual level, provides this capability in this
model
Current Efforts

Lexical Conceptual Structure (LCS)

Used as part of many MT language pairs
including ChinMT (Habash et al., 2003a)


Chinese-English
Also been used for other natural language
applications

Cross-language information retrieval
Current Efforts

Lexical Conceptual Structure (LCS)
Approach focuses on linguistic divergences
 For example – Conflational divergence

Arabic:
The reporter caused the email to go to
Al-Jazeera in a sending manner.
English: The reporter emailed Al-Jazeera.
Current Efforts

LCS representation
(event cause
(thing[agent] reporter+)
(go loc
(thing[theme] email+)
(path to loc
(thing email+)
(position at loc (thing email+) (thing[goal] aljazeera+)))
(manner send+ingly)))
Current Efforts

LCS representation
(event cause
(thing[agent] reporter+)
(go loc
(thing[theme] email+)
(path to loc
(thing email+)
(position at loc (thing email+) (thing[goal] aljazeera+)))
(manner send+ingly)))

Primary components of meaning are the top-level
conceptual nodes cause and go
Current Efforts

LCS representation
(event cause
(thing[agent] reporter+)
(go loc
(thing[theme] email+)
(path to loc
(thing email+)
(position at loc (thing email+) (thing[goal] aljazeera+)))
(manner send+ingly)))
Primary components of meaning are the top-level
conceptual nodes cause and go
 These are taken together with their arguments



Each identified by a semantic role
(agent, theme, goal)
And a modifier (manner) send+ingly
LCS as an interlingua?



Jackendoff wasn’t trying to capture all of
meaning – just the semantics that
corresponds to syntactic generalizations
Ch-of-loc, causation, states, ... are very
fundamental. If we don’t get anything else,
we should get at least these
LCS highlights just these relations – not bad
for an interlingua, but what about those
stylistic things, etc?
Current Efforts

Approximate Interlingua (Dorr and Habash,
2002)
 Depth of knowledge-based systems is
approximated
 Taps into the richness of resources in one
language (often English)
 This information is used to map the sourcelanguage input to the target-language output
Current Efforts

Approximate Interlingua (Dorr and Habash, 2002)
Focus on linguistic divergences but with fewer
knowledge-intensive components than in LCS
 Key feature

Coupling of basic argument-structure information with some,
but not all, components the LCS representation
 Only the top-level primitives and semantic roles are retained


This new representation provides the basis for
generation of multiple sentences that are statistically
pared down – ranked by TL constraints
Current Efforts

Approximate Interlingua representation:
Check top-level conceptual nodes for matches
 Check unmatched thematic roles for ‘conflatability’



Cases where semantic roles are absorbed into other
predicate positions
Here there is a relation between the conflated
argument EMAILN and EMAILV